{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用seaborn里的直方图distplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  ...     atemp       hum  windspeed  casual  registered   cnt\n",
       "0        1  2011-01-01       1   0     1        0  ...  0.363625  0.805833   0.160446     331         654   985\n",
       "1        2  2011-01-02       1   0     1        0  ...  0.353739  0.696087   0.248539     131         670   801\n",
       "2        3  2011-01-03       1   0     1        0  ...  0.189405  0.437273   0.248309     120        1229  1349\n",
       "3        4  2011-01-04       1   0     1        0  ...  0.212122  0.590435   0.160296     108        1454  1562\n",
       "4        5  2011-01-05       1   0     1        0  ...  0.229270  0.436957   0.186900      82        1518  1600\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# 读入数据\n",
    "train=pd.read_csv(\"day.csv\")\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train[\"casual\"],bins=30, kde=False)\n",
    "plt.xlabel(\"index of casual\", fontsize=12)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
